Overview

Dataset statistics

Number of variables13
Number of observations15360
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

target is highly correlated with gain_imb and 2 other fieldsHigh correlation
gain_imb is highly correlated with target and 5 other fieldsHigh correlation
iq_imb is highly correlated with gain_imb and 4 other fieldsHigh correlation
or_off is highly correlated with target and 5 other fieldsHigh correlation
quadr_err is highly correlated with targetHigh correlation
ph_err is highly correlated with gain_imb and 4 other fieldsHigh correlation
mag_err is highly correlated with gain_imb and 4 other fieldsHigh correlation
evm is highly correlated with gain_imb and 4 other fieldsHigh correlation
Tosc is highly correlated with TmixHigh correlation
Tmix is highly correlated with ToscHigh correlation
target is highly correlated with gain_imb and 2 other fieldsHigh correlation
gain_imb is highly correlated with target and 5 other fieldsHigh correlation
iq_imb is highly correlated with gain_imb and 3 other fieldsHigh correlation
or_off is highly correlated with target and 6 other fieldsHigh correlation
quadr_err is highly correlated with target and 4 other fieldsHigh correlation
ph_err is highly correlated with or_off and 2 other fieldsHigh correlation
mag_err is highly correlated with gain_imb and 5 other fieldsHigh correlation
evm is highly correlated with gain_imb and 5 other fieldsHigh correlation
Tosc is highly correlated with TmixHigh correlation
Tmix is highly correlated with ToscHigh correlation
target is highly correlated with quadr_errHigh correlation
gain_imb is highly correlated with or_off and 1 other fieldsHigh correlation
iq_imb is highly correlated with or_off and 3 other fieldsHigh correlation
or_off is highly correlated with gain_imb and 4 other fieldsHigh correlation
quadr_err is highly correlated with targetHigh correlation
ph_err is highly correlated with iq_imb and 3 other fieldsHigh correlation
mag_err is highly correlated with gain_imb and 4 other fieldsHigh correlation
evm is highly correlated with iq_imb and 3 other fieldsHigh correlation
Tosc is highly correlated with TmixHigh correlation
Tmix is highly correlated with ToscHigh correlation
target is highly correlated with cfo_demod and 7 other fieldsHigh correlation
cfo_demod is highly correlated with target and 6 other fieldsHigh correlation
gain_imb is highly correlated with target and 6 other fieldsHigh correlation
iq_imb is highly correlated with target and 7 other fieldsHigh correlation
or_off is highly correlated with target and 7 other fieldsHigh correlation
quadr_err is highly correlated with target and 8 other fieldsHigh correlation
m_power is highly correlated with target and 8 other fieldsHigh correlation
ph_err is highly correlated with quadr_err and 2 other fieldsHigh correlation
mag_err is highly correlated with target and 7 other fieldsHigh correlation
evm is highly correlated with target and 6 other fieldsHigh correlation
Tosc is highly correlated with cfo_demod and 2 other fieldsHigh correlation
Tmix is highly correlated with cfo_demod and 2 other fieldsHigh correlation
Unnamed: 0 is uniformly distributed Uniform
Unnamed: 0 has unique values Unique

Reproduction

Analysis started2023-03-23 14:56:47.217320
Analysis finished2023-03-23 14:57:33.871911
Duration46.65 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

target
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.496809896
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:34.013253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile8
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.288858353
Coefficient of variation (CV)0.5089960231
Kurtosis-1.236035054
Mean4.496809896
Median Absolute Deviation (MAD)2
Skewness0.0029423043
Sum69071
Variance5.238872562
MonotonicityNot monotonic
2023-03-23T14:57:34.319222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
41949
12.7%
21929
12.6%
71920
12.5%
61917
12.5%
31916
12.5%
11912
12.4%
81910
12.4%
51907
12.4%
ValueCountFrequency (%)
11912
12.4%
21929
12.6%
31916
12.5%
41949
12.7%
51907
12.4%
61917
12.5%
71920
12.5%
81910
12.4%
ValueCountFrequency (%)
81910
12.4%
71920
12.5%
61917
12.5%
51907
12.4%
41949
12.7%
31916
12.5%
21929
12.6%
11912
12.4%

Unnamed: 0
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct15360
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7679.5
Minimum0
Maximum15359
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:34.630238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile767.95
Q13839.75
median7679.5
Q311519.25
95-th percentile14591.05
Maximum15359
Range15359
Interquartile range (IQR)7679.5

Descriptive statistics

Standard deviation4434.194403
Coefficient of variation (CV)0.5774066544
Kurtosis-1.2
Mean7679.5
Median Absolute Deviation (MAD)3840
Skewness0
Sum117957120
Variance19662080
MonotonicityStrictly increasing
2023-03-23T14:57:34.861199image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
11
 
< 0.1%
102321
 
< 0.1%
102331
 
< 0.1%
102341
 
< 0.1%
102351
 
< 0.1%
102361
 
< 0.1%
102371
 
< 0.1%
102381
 
< 0.1%
102391
 
< 0.1%
Other values (15350)15350
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
153591
< 0.1%
153581
< 0.1%
153571
< 0.1%
153561
< 0.1%
153551
< 0.1%
153541
< 0.1%
153531
< 0.1%
153521
< 0.1%
153511
< 0.1%
153501
< 0.1%

cfo_demod
Real number (ℝ)

HIGH CORRELATION

Distinct15353
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-373.0873234
Minimum-1362.15271
Maximum737.5123901
Zeros0
Zeros (%)0.0%
Negative13163
Negative (%)85.7%
Memory size120.1 KiB
2023-03-23T14:57:35.149657image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1362.15271
5-th percentile-999.5207153
Q1-589.4571075
median-400.9930115
Q3-221.3521996
95-th percentile513.4410309
Maximum737.5123901
Range2099.6651
Interquartile range (IQR)368.104908

Descriptive statistics

Standard deviation411.288185
Coefficient of variation (CV)-1.102391208
Kurtosis0.2574354968
Mean-373.0873234
Median Absolute Deviation (MAD)184.4373779
Skewness0.5828809819
Sum-5730621.287
Variance169157.9711
MonotonicityNot monotonic
2023-03-23T14:57:35.642935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-337.62326052
 
< 0.1%
-1014.3314212
 
< 0.1%
-521.50402832
 
< 0.1%
605.41723632
 
< 0.1%
-325.75396732
 
< 0.1%
-456.0304262
 
< 0.1%
-431.60092162
 
< 0.1%
-929.19012451
 
< 0.1%
-403.89868161
 
< 0.1%
-477.98162841
 
< 0.1%
Other values (15343)15343
99.9%
ValueCountFrequency (%)
-1362.152711
< 0.1%
-1346.4505621
< 0.1%
-1342.0440671
< 0.1%
-1337.6789551
< 0.1%
-1332.0087891
< 0.1%
-1319.4058841
< 0.1%
-1304.2629391
< 0.1%
-1301.3411871
< 0.1%
-1300.2692871
< 0.1%
-1299.3070071
< 0.1%
ValueCountFrequency (%)
737.51239011
< 0.1%
736.15509031
< 0.1%
730.51855471
< 0.1%
726.89569091
< 0.1%
719.65954591
< 0.1%
712.15930181
< 0.1%
706.6857911
< 0.1%
702.88061521
< 0.1%
702.50213621
< 0.1%
699.74859621
< 0.1%

gain_imb
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct14474
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.04404946905
Minimum-0.1040313318
Maximum0.1763845086
Zeros0
Zeros (%)0.0%
Negative3663
Negative (%)23.8%
Memory size120.1 KiB
2023-03-23T14:57:35.933937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.1040313318
5-th percentile-0.04593597688
Q10.006846986592
median0.06036293134
Q30.07807246037
95-th percentile0.09828312024
Maximum0.1763845086
Range0.2804158405
Interquartile range (IQR)0.07122547378

Descriptive statistics

Standard deviation0.04665710198
Coefficient of variation (CV)1.059197829
Kurtosis-0.4640437682
Mean0.04404946905
Median Absolute Deviation (MAD)0.02312829159
Skewness-0.8055210002
Sum676.5998447
Variance0.002176885165
MonotonicityNot monotonic
2023-03-23T14:57:36.140799image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.06429722163
 
< 0.1%
0.073121555153
 
< 0.1%
0.036708354953
 
< 0.1%
0.068339653313
 
< 0.1%
0.059807650743
 
< 0.1%
0.082453884183
 
< 0.1%
0.054902140053
 
< 0.1%
0.062646411363
 
< 0.1%
0.04108621183
 
< 0.1%
0.068853303793
 
< 0.1%
Other values (14464)15330
99.8%
ValueCountFrequency (%)
-0.10403133181
< 0.1%
-0.10039321331
< 0.1%
-0.098381765191
< 0.1%
-0.096782267091
< 0.1%
-0.095443204051
< 0.1%
-0.094066664581
< 0.1%
-0.093832731251
< 0.1%
-0.093641713261
< 0.1%
-0.093027345841
< 0.1%
-0.089517273011
< 0.1%
ValueCountFrequency (%)
0.17638450861
< 0.1%
0.148423181
< 0.1%
0.13826271891
< 0.1%
0.13738626241
< 0.1%
0.13586957751
< 0.1%
0.13471251731
< 0.1%
0.13418439031
< 0.1%
0.13307809831
< 0.1%
0.13242138921
< 0.1%
0.13207159941
< 0.1%

iq_imb
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15318
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-35.21798113
Minimum-56.24979401
Maximum-28.42233467
Zeros0
Zeros (%)0.0%
Negative15360
Negative (%)100.0%
Memory size120.1 KiB
2023-03-23T14:57:36.348602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-56.24979401
5-th percentile-46.37988396
Q1-38.71220779
median-33.90198898
Q3-30.95821238
95-th percentile-29.41219721
Maximum-28.42233467
Range27.82745934
Interquartile range (IQR)7.753995419

Descriptive statistics

Standard deviation5.420185415
Coefficient of variation (CV)-0.1539039219
Kurtosis-0.07516166269
Mean-35.21798113
Median Absolute Deviation (MAD)3.809121132
Skewness-0.9363409598
Sum-540948.1901
Variance29.37840993
MonotonicityNot monotonic
2023-03-23T14:57:36.558299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-33.979526522
 
< 0.1%
-37.801071172
 
< 0.1%
-35.030506132
 
< 0.1%
-39.152534482
 
< 0.1%
-31.966329572
 
< 0.1%
-29.317363742
 
< 0.1%
-30.233312612
 
< 0.1%
-34.529888152
 
< 0.1%
-31.638164522
 
< 0.1%
-44.314430242
 
< 0.1%
Other values (15308)15340
99.9%
ValueCountFrequency (%)
-56.249794011
< 0.1%
-54.883106231
< 0.1%
-53.698020941
< 0.1%
-53.624164581
< 0.1%
-53.516925811
< 0.1%
-53.231361391
< 0.1%
-53.150859831
< 0.1%
-52.827526091
< 0.1%
-52.354938511
< 0.1%
-51.886314391
< 0.1%
ValueCountFrequency (%)
-28.422334671
< 0.1%
-28.536706921
< 0.1%
-28.541240691
< 0.1%
-28.550048831
< 0.1%
-28.550233841
< 0.1%
-28.575498581
< 0.1%
-28.603149411
< 0.1%
-28.606666561
< 0.1%
-28.612522131
< 0.1%
-28.619396211
< 0.1%

or_off
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15316
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-31.11750942
Minimum-40.76312637
Maximum-26.306427
Zeros0
Zeros (%)0.0%
Negative15360
Negative (%)100.0%
Memory size120.1 KiB
2023-03-23T14:57:36.766821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-40.76312637
5-th percentile-37.93847809
Q1-35.16326141
median-29.6711731
Q3-28.13811874
95-th percentile-27.11819115
Maximum-26.306427
Range14.45669937
Interquartile range (IQR)7.02514267

Descriptive statistics

Standard deviation3.724180256
Coefficient of variation (CV)-0.1196811803
Kurtosis-0.9066140142
Mean-31.11750942
Median Absolute Deviation (MAD)1.868984222
Skewness-0.7602602747
Sum-477964.9446
Variance13.86951858
MonotonicityNot monotonic
2023-03-23T14:57:36.976002image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-35.826183322
 
< 0.1%
-29.371614462
 
< 0.1%
-27.988698962
 
< 0.1%
-27.918056492
 
< 0.1%
-30.302179342
 
< 0.1%
-31.996341712
 
< 0.1%
-28.489313132
 
< 0.1%
-29.26518252
 
< 0.1%
-31.448495862
 
< 0.1%
-37.914367682
 
< 0.1%
Other values (15306)15340
99.9%
ValueCountFrequency (%)
-40.763126371
< 0.1%
-40.577705381
< 0.1%
-40.502006531
< 0.1%
-40.338405611
< 0.1%
-40.285102841
< 0.1%
-40.275878911
< 0.1%
-40.26630021
< 0.1%
-40.258041381
< 0.1%
-40.251308441
< 0.1%
-40.197868351
< 0.1%
ValueCountFrequency (%)
-26.3064271
< 0.1%
-26.425933841
< 0.1%
-26.458242421
< 0.1%
-26.487962721
< 0.1%
-26.517200471
< 0.1%
-26.548038481
< 0.1%
-26.567436221
< 0.1%
-26.607084271
< 0.1%
-26.611082081
< 0.1%
-26.621610641
< 0.1%

quadr_err
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15315
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4027559814
Minimum-4.31816864
Maximum3.340285301
Zeros0
Zeros (%)0.0%
Negative3889
Negative (%)25.3%
Memory size120.1 KiB
2023-03-23T14:57:37.200675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-4.31816864
5-th percentile-3.868311465
Q1-2.607032001
median1.214601398
Q32.25017345
95-th percentile3.034231699
Maximum3.340285301
Range7.658453941
Interquartile range (IQR)4.857205451

Descriptive statistics

Standard deviation2.509561229
Coefficient of variation (CV)6.230971965
Kurtosis-0.9155509215
Mean0.4027559814
Median Absolute Deviation (MAD)1.209864736
Skewness-0.8103288982
Sum6186.331874
Variance6.297897561
MonotonicityNot monotonic
2023-03-23T14:57:37.424850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.909284832
 
< 0.1%
3.1986048222
 
< 0.1%
1.188736322
 
< 0.1%
2.8697073462
 
< 0.1%
1.0528702742
 
< 0.1%
1.2161663772
 
< 0.1%
3.0627081392
 
< 0.1%
-3.6695840362
 
< 0.1%
2.8686299322
 
< 0.1%
1.3071445232
 
< 0.1%
Other values (15305)15340
99.9%
ValueCountFrequency (%)
-4.318168641
< 0.1%
-4.2836489681
< 0.1%
-4.2711052891
< 0.1%
-4.2662725451
< 0.1%
-4.2653131481
< 0.1%
-4.254019261
< 0.1%
-4.2448158261
< 0.1%
-4.2396817211
< 0.1%
-4.2350168231
< 0.1%
-4.2340002061
< 0.1%
ValueCountFrequency (%)
3.3402853011
< 0.1%
3.3106369971
< 0.1%
3.2949912551
< 0.1%
3.2656466961
< 0.1%
3.2637989521
< 0.1%
3.2573206421
< 0.1%
3.2514705661
< 0.1%
3.2487611771
< 0.1%
3.2436432841
< 0.1%
3.2394425871
< 0.1%

m_power
Real number (ℝ)

HIGH CORRELATION

Distinct15333
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.4548105414
Minimum-1.814462066
Maximum1.179036021
Zeros0
Zeros (%)0.0%
Negative11208
Negative (%)73.0%
Memory size120.1 KiB
2023-03-23T14:57:37.635822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.814462066
5-th percentile-1.278335214
Q1-0.9420369118
median-0.5761074722
Q30.05957408994
95-th percentile0.5870859027
Maximum1.179036021
Range2.993498087
Interquartile range (IQR)1.001611002

Descriptive statistics

Standard deviation0.5990938745
Coefficient of variation (CV)-1.317238322
Kurtosis-0.8218757182
Mean-0.4548105414
Median Absolute Deviation (MAD)0.4380898625
Skewness0.402595062
Sum-6985.889916
Variance0.3589134705
MonotonicityNot monotonic
2023-03-23T14:57:37.840123image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.99576437472
 
< 0.1%
-1.012823822
 
< 0.1%
-1.1044569022
 
< 0.1%
-1.2492314582
 
< 0.1%
-1.0717476612
 
< 0.1%
-1.0856677292
 
< 0.1%
-0.80551218992
 
< 0.1%
-0.80028682952
 
< 0.1%
-0.94473475222
 
< 0.1%
-0.94916045672
 
< 0.1%
Other values (15323)15340
99.9%
ValueCountFrequency (%)
-1.8144620661
< 0.1%
-1.7936087851
< 0.1%
-1.7616084811
< 0.1%
-1.7525367741
< 0.1%
-1.7469201091
< 0.1%
-1.7323700191
< 0.1%
-1.7288664581
< 0.1%
-1.7073671821
< 0.1%
-1.7005705831
< 0.1%
-1.6934959891
< 0.1%
ValueCountFrequency (%)
1.1790360211
< 0.1%
1.1465312241
< 0.1%
1.0965739491
< 0.1%
1.0949875121
< 0.1%
1.0800045731
< 0.1%
1.0794146061
< 0.1%
1.0679997211
< 0.1%
1.0668325421
< 0.1%
1.0620043281
< 0.1%
1.058025361
< 0.1%

ph_err
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15330
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.230880244
Minimum0.8557155728
Maximum11.43954086
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:38.055660image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.8557155728
5-th percentile1.014047015
Q11.102672756
median1.207846105
Q31.307768941
95-th percentile1.536017781
Maximum11.43954086
Range10.58382529
Interquartile range (IQR)0.2050961852

Descriptive statistics

Standard deviation0.2261381055
Coefficient of variation (CV)0.1837206394
Kurtosis455.3260253
Mean1.230880244
Median Absolute Deviation (MAD)0.1030801535
Skewness14.29470441
Sum18906.32055
Variance0.05113844276
MonotonicityNot monotonic
2023-03-23T14:57:38.271208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0902274852
 
< 0.1%
1.170194032
 
< 0.1%
1.4430768492
 
< 0.1%
1.0843055252
 
< 0.1%
1.0376262662
 
< 0.1%
1.1908265352
 
< 0.1%
1.0706944472
 
< 0.1%
1.196877482
 
< 0.1%
1.234380962
 
< 0.1%
1.0642073152
 
< 0.1%
Other values (15320)15340
99.9%
ValueCountFrequency (%)
0.85571557281
< 0.1%
0.86264991761
< 0.1%
0.86556428671
< 0.1%
0.90463030341
< 0.1%
0.90696448091
< 0.1%
0.90909165141
< 0.1%
0.91092687841
< 0.1%
0.91098737721
< 0.1%
0.91232275961
< 0.1%
0.91287070511
< 0.1%
ValueCountFrequency (%)
11.439540861
< 0.1%
8.1544866561
< 0.1%
7.1342339521
< 0.1%
6.5873842241
< 0.1%
6.5873732571
< 0.1%
5.9551687241
< 0.1%
5.8437705041
< 0.1%
5.6703138351
< 0.1%
5.5142264371
< 0.1%
4.9316396711
< 0.1%

mag_err
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15357
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.830120351
Minimum0.3870858252
Maximum10.59581089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:38.493278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.3870858252
5-th percentile0.49342563
Q11.010115623
median1.681543052
Q32.394056916
95-th percentile3.564314473
Maximum10.59581089
Range10.20872506
Interquartile range (IQR)1.383941293

Descriptive statistics

Standard deviation0.9379499294
Coefficient of variation (CV)0.5125072398
Kurtosis-0.3400758016
Mean1.830120351
Median Absolute Deviation (MAD)0.6731712818
Skewness0.3983743155
Sum28110.64859
Variance0.8797500701
MonotonicityNot monotonic
2023-03-23T14:57:38.717508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.503628852
 
< 0.1%
2.1370253562
 
< 0.1%
1.012609722
 
< 0.1%
2.1462130551
 
< 0.1%
1.5519524811
 
< 0.1%
2.2434604171
 
< 0.1%
1.6534067391
 
< 0.1%
3.6193635461
 
< 0.1%
0.8321007491
 
< 0.1%
2.6454977991
 
< 0.1%
Other values (15347)15347
99.9%
ValueCountFrequency (%)
0.38708582521
< 0.1%
0.38824075461
< 0.1%
0.3935246171
< 0.1%
0.39572045211
< 0.1%
0.40024939181
< 0.1%
0.40294969081
< 0.1%
0.40467008951
< 0.1%
0.4056362511
< 0.1%
0.40587741141
< 0.1%
0.40623173121
< 0.1%
ValueCountFrequency (%)
10.595810891
< 0.1%
5.0172305111
< 0.1%
3.9422993661
< 0.1%
3.9176740651
< 0.1%
3.9072184561
< 0.1%
3.9067893031
< 0.1%
3.8934149741
< 0.1%
3.8905830381
< 0.1%
3.8844532971
< 0.1%
3.8788254261
< 0.1%

evm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15337
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.838790749
Minimum1.645375371
Maximum19.15297127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:38.922051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1.645375371
5-th percentile1.867495209
Q12.226912796
median2.716616392
Q33.30206728
95-th percentile4.278235531
Maximum19.15297127
Range17.5075959
Interquartile range (IQR)1.075154483

Descriptive statistics

Standard deviation0.7920699761
Coefficient of variation (CV)0.2790166821
Kurtosis21.12854868
Mean2.838790749
Median Absolute Deviation (MAD)0.5420582294
Skewness1.928154947
Sum43603.82591
Variance0.6273748471
MonotonicityNot monotonic
2023-03-23T14:57:39.139553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.3998134142
 
< 0.1%
3.435935022
 
< 0.1%
2.4491024022
 
< 0.1%
4.1989364622
 
< 0.1%
2.4147677422
 
< 0.1%
2.2913970952
 
< 0.1%
3.2270748622
 
< 0.1%
2.4530606272
 
< 0.1%
3.3564877512
 
< 0.1%
3.3014302252
 
< 0.1%
Other values (15327)15340
99.9%
ValueCountFrequency (%)
1.6453753711
< 0.1%
1.6562832591
< 0.1%
1.6585714821
< 0.1%
1.6600828171
< 0.1%
1.6646779781
< 0.1%
1.6680849791
< 0.1%
1.6680918931
< 0.1%
1.6685194971
< 0.1%
1.6694754361
< 0.1%
1.6728514431
< 0.1%
ValueCountFrequency (%)
19.152971271
< 0.1%
14.830652241
< 0.1%
12.830112461
< 0.1%
12.064522741
< 0.1%
11.866350171
< 0.1%
10.833746911
< 0.1%
10.760110861
< 0.1%
10.334991461
< 0.1%
10.202612881
< 0.1%
8.973271371
< 0.1%

Tosc
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct585
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.10541667
Minimum-0.6
Maximum58.4
Zeros5
Zeros (%)< 0.1%
Negative95
Negative (%)0.6%
Memory size120.1 KiB
2023-03-23T14:57:39.355429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-0.6
5-th percentile1.8
Q113.4
median25.2
Q338.6
95-th percentile54.2
Maximum58.4
Range59
Interquartile range (IQR)25.2

Descriptive statistics

Standard deviation16.11446197
Coefficient of variation (CV)0.6172842277
Kurtosis-0.9791473827
Mean26.10541667
Median Absolute Deviation (MAD)12.6
Skewness0.2075194392
Sum400979.2
Variance259.6758845
MonotonicityNot monotonic
2023-03-23T14:57:39.589209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.1144
 
0.9%
55.1128
 
0.8%
0.6126
 
0.8%
3.4118
 
0.8%
19.1102
 
0.7%
1.8102
 
0.7%
54.8100
 
0.7%
19.996
 
0.6%
26.395
 
0.6%
54.691
 
0.6%
Other values (575)14258
92.8%
ValueCountFrequency (%)
-0.65
 
< 0.1%
-0.551
0.3%
-0.428
 
0.2%
-0.36
 
< 0.1%
-0.25
 
< 0.1%
05
 
< 0.1%
0.25
 
< 0.1%
0.337
0.2%
0.490
0.6%
0.525
 
0.2%
ValueCountFrequency (%)
58.45
< 0.1%
58.311
0.1%
58.24
 
< 0.1%
58.14
 
< 0.1%
584
 
< 0.1%
57.910
0.1%
57.89
0.1%
57.67
< 0.1%
57.48
0.1%
57.36
< 0.1%

Tmix
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct572
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.98771484
Minimum6.9
Maximum64.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size120.1 KiB
2023-03-23T14:57:39.797398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum6.9
5-th percentile9.8
Q121.6
median35.3
Q347.8
95-th percentile61.6
Maximum64.7
Range57.8
Interquartile range (IQR)26.2

Descriptive statistics

Standard deviation16.14759947
Coefficient of variation (CV)0.461521981
Kurtosis-1.09350187
Mean34.98771484
Median Absolute Deviation (MAD)13.2
Skewness0.06512981784
Sum537411.3
Variance260.7449685
MonotonicityNot monotonic
2023-03-23T14:57:40.005419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8148
 
1.0%
9.4115
 
0.7%
9.292
 
0.6%
8.990
 
0.6%
35.885
 
0.6%
35.671
 
0.5%
62.871
 
0.5%
21.168
 
0.4%
10.368
 
0.4%
63.366
 
0.4%
Other values (562)14486
94.3%
ValueCountFrequency (%)
6.915
 
0.1%
744
0.3%
7.133
0.2%
7.310
 
0.1%
7.44
 
< 0.1%
7.56
 
< 0.1%
7.63
 
< 0.1%
7.86
 
< 0.1%
7.95
 
< 0.1%
8.14
 
< 0.1%
ValueCountFrequency (%)
64.74
 
< 0.1%
64.610
0.1%
64.55
 
< 0.1%
64.414
0.1%
64.38
 
0.1%
64.123
0.1%
646
 
< 0.1%
63.97
 
< 0.1%
63.813
0.1%
63.69
 
0.1%

Interactions

2023-03-23T14:57:28.313025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:51.140212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:56.332236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:59.147995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.353862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:03.771575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.558943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.880779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.214752image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:15.034054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:18.692127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:22.291712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.534739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:28.635974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:51.799118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:56.521828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:59.335729image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.533359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:04.006988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.756054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.070514image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.400756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:15.327706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:18.949448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:22.464406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.696283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:28.832044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:52.512232image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:56.698030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:59.520822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.708349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:04.212266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.942672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.262847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.591049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:15.585155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:19.223103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:22.733315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.872468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:29.028276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:52.792120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:56.856430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:59.674683image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.862659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:04.458725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:08.110265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.427386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.752628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:15.912707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:19.449579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:23.058385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:26.037700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:29.213962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:53.362864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:57.090750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:59.836011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.039043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:05.644745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:08.289609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.603288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.928741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:16.217676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:19.690095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:23.439138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:26.263762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:29.389396image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:53.666691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:57.359462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.002438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.214172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:05.882470image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:08.464186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.784609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.100393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:16.442798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:20.181850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:23.737365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:26.497070image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:29.938929image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:54.041412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:57.859158image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.182500image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.394349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:06.112784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:08.646790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:10.977947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.285508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:17.005364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:20.503111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:24.163424image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:26.738149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:30.151956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:54.405121image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.088317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.357676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.576680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:06.398162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:08.842905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:11.164397image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.465162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:17.334183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:20.725050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:24.479733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:26.922024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:30.457620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:54.844598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.268890image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.527230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.741635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:06.615269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.017932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:11.336419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.630584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:17.568707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:20.897788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:24.651964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:27.109763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:30.724305image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:55.156273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.437695image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.684786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:02.905228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:06.838363image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.184082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:11.501287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.800629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:17.751441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:21.062138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:24.821013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:27.330013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:30.966896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:55.524539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.610800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:00.843206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:03.077531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.015945image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.351250image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:11.661243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:13.980151image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:17.955117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:21.372722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.004206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:27.548474image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:31.238628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:55.903956image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.786472image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.021591image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:03.273596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.199209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.532635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:11.850976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:14.169690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:18.215969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:21.734725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.192758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:27.771734image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:31.472798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:56.162941image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:56:58.965390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:01.195290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:03.529319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:07.390784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:09.706757image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:12.035519image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:14.776028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:18.476192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:22.079269image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:25.368007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-03-23T14:57:28.005993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-03-23T14:57:40.189326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-03-23T14:57:40.435317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-03-23T14:57:40.655109image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-03-23T14:57:40.880796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2023-03-23T14:57:32.269324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-23T14:57:33.519398image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

targetUnnamed: 0cfo_demodgain_imbiq_imbor_offquadr_errm_powerph_errmag_errevmToscTmix
050592.2348020.048079-35.082729-28.5608461.993170-0.4997211.1079261.5075502.42394339.947.6
111-103.302032-0.019917-29.946953-35.798664-3.642311-0.9281931.2360592.7415683.45805614.823.1
262-582.3312990.036379-32.096672-31.9056282.835839-1.2724851.2821632.1400963.01352242.548.6
333-630.6112670.063928-38.216297-30.0841711.346316-0.5964381.1548481.0934652.25451426.135.4
424-415.526978-0.055761-29.180740-36.601025-3.9635260.1130551.4988893.6087374.28668424.240.8
525-432.158600-0.030759-29.860676-36.194633-3.6754410.2434031.5374983.4499374.19493127.544.8
676-1121.1724850.080203-39.686409-27.7863041.063713-0.1119711.1975570.9001882.07962556.864.1
787-60.4082070.097589-31.729502-29.7075352.926517-0.3094861.3004292.1702983.13008611.118.5
888-162.6842960.117599-32.867283-28.4128972.533771-0.7610691.2597921.9390592.87345847.955.0
939-460.1153560.097939-38.504665-30.3810961.198071-0.3545721.2014201.0405332.31137810.617.6

Last rows

targetUnnamed: 0cfo_demodgain_imbiq_imbor_offquadr_errm_powerph_errmag_errevmToscTmix
15350615350-472.3382260.028677-30.780327-33.2921833.310637-0.6895031.4839232.3442343.4817762.89.7
15351115351-381.7114870.001134-30.604177-36.355850-3.379224-1.0641281.2220642.4862143.26266841.451.8
15352515352221.2912750.066242-35.113914-28.2678681.962977-0.5327361.1038811.3979922.36247354.863.1
15353815353-372.7196960.047038-32.398685-28.9887162.731194-0.6917501.2957362.0409533.00342427.336.0
15354515354561.5279540.080071-34.625835-28.7078702.060631-0.0025781.1072021.6053892.47642920.027.8
15355315355-476.5756530.039874-39.792461-29.9629971.143736-0.6883591.1755171.0446362.22431124.633.8
15356815356-345.6455080.077925-32.963398-27.9984442.524010-1.1353761.1593561.9489502.76140055.162.7
15357715357-613.9898070.090771-38.951530-28.1119881.1458930.3521510.9958570.9383081.93981839.546.9
15358715358-997.7695310.099840-39.532818-27.8198261.0141360.0403981.0697360.8759862.02606251.758.4
15359515359450.4468380.068571-34.959385-28.3461761.996514-0.3683851.0356481.5274752.35793845.452.9